A generalised optimisation method for double pulsed particle image velocimetry

A new method is presented which allows for the optimisation of a double pulsed particle image velocimetry (PIV) system given a priori information on the dynamic range and velocity gradients at a chosen local region in the flow. The method is developed from considerations of the worst case signal-to-noise characteristics corresponding to the autocorrelation function obtained from this region. Optimisation permits the user to define a window of operation for a PIV system which, providing a minimum level of seeding is present, will ensure that from a series of experiments on average a minimum of 50% valid vectors will be obtained from the chosen region. Validation of the method was carried out by using a Monte Carlo simulation based on the theoretical model. Optimisation and Monte Carlo prediction were shown to agree to within 1.5%.